Çoklu beyin yapilarinin baǧlaşik, parametrik olmayan şekil önbilgisi kullanilarak bölütlenmesi

Translated title of the contribution: Segmentation of multiple brain structures using coupled nonparametric shape priors

M. Gökhan Uzunbaş, Müjdat Çetin, Gözde Ünal, Aytül Erçil

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

This paper presents a new approach for segmentation of multiple brain structures. We introduce a new coupled shape prior for neighboring structures in magnetic resonance images (MRI) for multi object segmentation problem, where the information obtained from images can not provide enough contrast or exact boundary. In segmentation of low contrasted brain structures we take the advantage of using prior information enforced by interaction between neighboring structures in a nonparametric estimation fashion. Using nonparametric density estimation of multiple shapes, we introduce the coupled shape prior information into the segmentation process which is based on active contour models. We demonstrate the effectiveness of our method on real magnetic resonance images in challenging segmentation scenarios where existing methods fail.

Translated title of the contributionSegmentation of multiple brain structures using coupled nonparametric shape priors
Original languageTurkish
Title of host publication2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU - Aydin, Turkey
Duration: 20 Apr 200822 Apr 2008

Publication series

Name2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU

Conference

Conference2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU
Country/TerritoryTurkey
CityAydin
Period20/04/0822/04/08

Fingerprint

Dive into the research topics of 'Segmentation of multiple brain structures using coupled nonparametric shape priors'. Together they form a unique fingerprint.

Cite this